--- layout: default title: Xons/Single cell RNAseq Demo ---
single cell RNAseq
single end
non-stranded
50 bp reads
Drosophila
count >= 1
expression of detected genes (count>=1)
Mapped reads >= 1 million
Percentage of mitochondrial genes <= 10%
Detected gene >= 4000
counts of GFP > 0
This is depends on how rare the sub-population we are looking for. Here we hope the population are more than 5 cells. Commonly expressed gene (count>=5) in cells >=5
Use protein coding gene only for downstream analysis.
use RUV package
Marker gene expression
p<1e-5, N=1599
use Seurat package
t-SNE using 7 PCs
R version 3.3.1 (2016-06-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)
locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets [8] methods base
other attached packages: [1] Seurat_1.4.0.6 cowplot_0.7.0
[3] statmod_1.4.26 genefilter_1.54.2
[5] DESeq_1.24.0 locfit_1.5-9.1
[7] RUVSeq_1.6.2 edgeR_3.14.0
[9] limma_3.28.21 EDASeq_2.6.2
[11] ShortRead_1.30.0 BiocParallel_1.6.6
[13] lattice_0.20-34 GenomicFeatures_1.24.5
[15] AnnotationDbi_1.34.4 GenomicAlignments_1.8.4
[17] Rsamtools_1.24.0 Biostrings_2.40.2
[19] XVector_0.12.1 SummarizedExperiment_1.2.3 [21] Biobase_2.32.0 GenomicRanges_1.24.3
[23] GenomeInfoDb_1.8.7 IRanges_2.6.1
[25] S4Vectors_0.10.3 BiocGenerics_0.18.0
[27] knitr_1.14 pander_0.6.0
[29] scales_0.4.1 pheatmap_1.0.8
[31] RColorBrewer_1.1-2 reshape2_1.4.2
[33] ggplot2_2.2.0 rmarkdown_1.1
loaded via a namespace (and not attached): [1] Rtsne_0.11 VGAM_1.0-2 minqa_1.2.4
[4] colorspace_1.2-7 hwriter_1.3.2 class_7.3-14
[7] modeltools_0.2-21 mclust_5.2 MatrixModels_0.4-1 [10] flexmix_2.3-13 mvtnorm_1.0-5 ranger_0.6.0
[13] codetools_0.2-15 splines_3.3.1 R.methodsS3_1.7.1
[16] mnormt_1.5-5 robustbase_0.92-6 geneplotter_1.50.0 [19] tclust_1.2-3 nloptr_1.0.4 caret_6.0-73
[22] pbkrtest_0.4-6 annotate_1.50.1 cluster_2.0.5
[25] kernlab_0.9-25 R.oo_1.21.0 assertthat_0.1
[28] Matrix_1.2-7.1 lazyeval_0.2.0 formatR_1.4
[31] lars_1.2 htmltools_0.3.5 quantreg_5.29
[34] tools_3.3.1 igraph_1.0.1 gtable_0.2.0
[37] dplyr_0.5.0 Rcpp_0.12.8 trimcluster_0.1-2
[40] gdata_2.17.0 ape_4.0 nlme_3.1-128
[43] rtracklayer_1.32.2 iterators_1.0.8 fpc_2.1-10
[46] stringr_1.1.0 lme4_1.1-12 irlba_2.1.2
[49] gtools_3.5.0 XML_3.98-1.4 DEoptimR_1.0-6
[52] zlibbioc_1.18.0 MASS_7.3-45 aroma.light_3.2.0
[55] SparseM_1.72 yaml_2.1.13 pbapply_1.3-1
[58] gridExtra_2.2.1 segmented_0.5-1.4 biomaRt_2.28.0
[61] fastICA_1.2-0 latticeExtra_0.6-28 stringi_1.1.2
[64] RSQLite_1.0.0 foreach_1.4.3 caTools_1.17.1
[67] boot_1.3-18 prabclus_2.2-6 matrixStats_0.51.0 [70] bitops_1.0-6 evaluate_0.10 ROCR_1.0-7
[73] labeling_0.3 R6_2.2.0 plyr_1.8.4
[76] magrittr_1.5 gplots_3.0.1 DBI_0.5-1
[79] sn_1.4-0 mgcv_1.8-16 mixtools_1.0.4
[82] survival_2.40-1 RCurl_1.96-0 nnet_7.3-12
[85] tibble_1.2 tsne_0.1-3 car_2.1-4
[88] KernSmooth_2.23-15 grid_3.3.1 FNN_1.1
[91] ModelMetrics_1.1.0 digest_0.6.10 diptest_0.75-7
[94] xtable_1.8-2 numDeriv_2016.8-1 R.utils_2.5.0
[97] munsell_0.4.3